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Open Access Research Article

Channel Capacity Bounds in the Presence of Quantized Channel State Information

Behrooz Makki*, Lotfollah Beygi and Thomas Eriksson

Author Affiliations

Department of Signals and Systems, Chalmers University of Technology, 41296, Gothenburg, Sweden

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EURASIP Journal on Wireless Communications and Networking 2010, 2010:495014  doi:10.1155/2010/495014

The electronic version of this article is the complete one and can be found online at: http://jwcn.eurasipjournals.com/content/2010/1/495014


Received:13 April 2010
Accepted:1 December 2010
Published:14 December 2010

© 2010 Behrooz Makki et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The goal of this paper is to investigate the effect of channel side information on increasing the achievable rates of continuous power-limited non-Gaussian channels. We focus on the case where (1) there is imperfect channel quality information available to the transmitter and the receiver and (2) while the channel gain is continuously varying, there are few cross-region changes, and the noise characteristics remain in each detection region for a long time. The results are presented for two scenarios, namely, reliable and unreliable region detection. Considering short- and long-term power constraints, the capacity bounds are found for log-normal and two different Nakagami-based channel distributions, and for both Max-Lloyd and equal probability quantization approaches. Then, the optimal gain partitioning approach, maximizing the achievable rates, is determined. Finally, general equations for the channel capacity bounds and optimal channel partitioning in the case of unreliable region detection are presented. Interestingly, the results show that, for high SNR's, it is possible to determine a power-independent optimal gain partitioning approach maximizing the capacity lower bound which, in both scenarios, is identical for both short- and long-term power constraints.

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